Solves several key problems:
…In the beginning of the year 1666…I procured me a Triangular glass-Prisme, to try therewith the celebrated Phaenomena of Colours. And in order thereto having darkened my chamber, and made a small hole in my window-shuts, to let in a convenient quantity of the Suns light, I placed my Prisme at his entrance, that it might be thereby refracted to the opposite wall. It was at first a very pleasing divertisement, to view the vivid and intense colours produced thereby
Garijo et al; 2013 (Phil Bourne’s lab, NIH Data Science Director)
https://storify.com/hlapp/reproducibility-repeatability-bigthink
.@weecology @cboettig @LorenaABarba The @NESCent informatics team takeaway today: even reproduction-ready papers are hard to reproduce.
— Todd Vision (@tjvision) February 18, 2014
30% of results using STRUCTURE not reproduced, even when data and code were avialable: https://doi.org/10.1111/j.1365-294X.2012.05754.x
http://doi.org/10.1080/00031305.2017.1375986
DESCRIPTION
README.md
.travis.md
R/
analysis/
|
├── paper/
│ ├── paper.Rmd # this is the main document to edit
│ ├── references.bib # this contains the reference list information
│ └── journal-of-archaeological-science.csl
| # this sets the style of citations & reference list
├── figures/
|
├── data/
│ ├── raw_data/ # data obtained from elswhere
│ └── derived_data/ # data generated during the analysis
|
└── templates
├── template.docx # used to style the output of the paper.Rmd
└── template.Rmd
debian:stable, CRAN snapshotsMyth 3: We need new platforms for reproducible computational science
http://ivory.idyll.org/blog/2014-myths-of-computational-reproducibility.html
Well-designed tools make a big a difference:
Significant usability gaps remain!
Myth 5: GUIs are the way to go, because scientists might actually use easy-to-use software.
Stodden (IASSIST 2010) sampled American academics registered at the Machine Learning conference NIPS (134 responses from 593 requests (23%). Red = communitarian norms, Blue = private incentives
Stodden (IASSIST 2010) sampled American academics registered at the Machine Learning conference NIPS (134 responses from 593 requests (23%). Red = communitarian norms, Blue = private incentives